Question

You have been asked to determine the goodness of fit for our power equation. The sum of squared errors (SSE) is 0.02, and there are 10 data points. What is the standard error of the estimate (SEE) and what does it mean?

Answer 1

The standard error of the estimate (SEE) measures the spread of observed data points around the line of best fit. It is calculated by taking the square root of the sum of squared errors (SSE) divided by the number of data points minus 2. In this case, the SEE is 0.0707.

Step-by-step explanation:

The standard error of the estimate (SEE) is a measure of the variability or spread of the observed data points around the line of best fit. It indicates how well the regression line represents the data. To calculate the standard error of the estimate, you take the square root of the sum of squared errors (SSE) divided by the number of data points minus 2.In this case, the SSE is 0.02 and there are 10 data points. So the SEE can be calculated as follows: SEE = sqrt(SSE / (n - 2)) = sqrt(0.02 / 8) = 0.0707.This means that, on average, the predicted values from the regression line will be off by approximately 0.0707 units from the actual values.